Abstract

Optical architectures for fully connected and limited-fan-out
space-variant weighted interconnections based on diffractive optical
elements for fixed-connection multilayer neural networks are
investigated and compared in terms of propagation lengths, system
volumes, connection densities, and interconnection cross talk. For
a small overall system volume the limited-fan-out architecture can
accommodate a much larger number of input and output
nodes. However, the interconnection cross talk of the
limited-fan-out space-variant architecture is relatively high owing to
noise from the diffractive-optical-element
reconstructions. Therefore a cross-talk reduction technique based
on a modified design procedure for diffractive optical elements is
proposed. It rearranges the reconstruction pattern of the
diffractive optical elements such that less noise lands on each
detector region. This technique is verified by the simulation of
one layer of an interconnection system with 128 × 128 input
nodes, 128 × 128 output nodes, and weighted connections that fan
out from each input node to the nearest 5 × 5 array of output
nodes. In addition to a significant cross-talk reduction, this
technique can reduce the propagation length and system
volume.

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